Carbon stock estimation in a Brazilian mangrove using optical satellite data.

The research proposes a model to estimate the carbon stock in mangrove forests from multispectral images from Landsat 8 and Sentinel 2B satellites. The Gramame River mangrove, located on the southern coast of Paraíba State, Brazil, was adopted as the study area. Carbon stocks in biomass, below and above ground, were measured from a forest inventory, and vegetation indices were processed on the Google Earth Engine (GEE) platform. To define the fit curves, linear and non-linear regressions were used. The choice of the model considered the highest coefficients of determination (R), the biomass and carbon stock were estimated from the equations. The biomass carbon stock, calculated from field data, corresponded to 22.27 Gg C, equivalent to 81.75 Gg CO, with 13.85 Gg C (50.84 Gg CO) and 8.42 Gg C (30.91 Gg CO) stored in biomass above and below ground, respectively. Among the models fitted to the indices calculated from Landsat 8 images, NDVI was the one that best explained the spatial distribution of biomass and carbon, with 90.26%. For Sentinel 2B, SAVI was able to explain 80.76%. The total estimated plant carbon stocks corresponded to 26.66 Gg (16.20 Gg C above and 10.36 Gg C below ground) for Landsat 8 and 27.76 Gg C (16.93 Gg C above and 10.83 Gg C below ground) for Sentinel 2B. The proposed work methodology and the suggested mathematical models can be replicated to analyze carbon stocks in other locations, especially in the Americas, because they share the same species.